Coordinating Attention in Face‐to‐Face Collaboration: The Dynamics of Gaze, Pointing, and Verbal Reference
Bibliographic record
Abstract
During real-world interactions, people rely on gaze, gestures, and verbal references to coordinate attention and establish shared understanding. Yet, it remains unclear if and how these modalities couple within and between interacting individuals in face-to-face settings. The current study addressed this issue by analyzing dyadic face-to-face interactions, where participants (n = 52) collaboratively ranked paintings while their gaze, pointing gestures, and verbal references were recorded. Using cross-recurrence quantification analysis, we found that participants readily used pointing gestures to complement gaze and verbal reference cues and that gaze directed toward the partner followed canonical conversational patterns, that is, more looks to the other's face when listening than speaking. Further, gaze, pointing, and verbal references showed significant coupling both within and between individuals, with pointing gestures and verbal references guiding the partner's gaze to shared targets and speaker gaze leading listener gaze. Moreover, simultaneous pointing and verbal referencing led to more sustained attention coupling compared to pointing alone. These findings highlight the multimodal nature of joint attention coordination, extending theories of embodied, interactive cognition by demonstrating how gaze, gestures, and language dynamically integrate into a shared cognitive system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".